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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code>.CCA</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-decomposition-cca">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cross_decomposition.CCA</span></code></a></li>
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  <div class="section" id="sklearn-cross-decomposition-cca">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code></a>.CCA<a class="headerlink" href="#sklearn-cross-decomposition-cca" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_decomposition.CCA">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cross_decomposition.</code><code class="sig-name descname">CCA</code><span class="sig-paren">(</span><em class="sig-param">n_components=2</em>, <em class="sig-param">scale=True</em>, <em class="sig-param">max_iter=500</em>, <em class="sig-param">tol=1e-06</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_cca.py#L7"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA" title="Permalink to this definition">¶</a></dt>
<dd><p>CCA Canonical Correlation Analysis.</p>
<p>CCA inherits from PLS with mode=”B” and deflation_mode=”canonical”.</p>
<p>Read more in the <a class="reference internal" href="../cross_decomposition.html#cross-decomposition"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_components</strong><span class="classifier">int, (default 2).</span></dt><dd><p>number of components to keep.</p>
</dd>
<dt><strong>scale</strong><span class="classifier">boolean, (default True)</span></dt><dd><p>whether to scale the data?</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">an integer, (default 500)</span></dt><dd><p>the maximum number of iterations of the NIPALS inner loop</p>
</dd>
<dt><strong>tol</strong><span class="classifier">non-negative real, default 1e-06.</span></dt><dd><p>the tolerance used in the iterative algorithm</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean</span></dt><dd><p>Whether the deflation be done on a copy. Let the default value
to True unless you don’t care about side effects</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>x_weights_</strong><span class="classifier">array, [p, n_components]</span></dt><dd><p>X block weights vectors.</p>
</dd>
<dt><strong>y_weights_</strong><span class="classifier">array, [q, n_components]</span></dt><dd><p>Y block weights vectors.</p>
</dd>
<dt><strong>x_loadings_</strong><span class="classifier">array, [p, n_components]</span></dt><dd><p>X block loadings vectors.</p>
</dd>
<dt><strong>y_loadings_</strong><span class="classifier">array, [q, n_components]</span></dt><dd><p>Y block loadings vectors.</p>
</dd>
<dt><strong>x_scores_</strong><span class="classifier">array, [n_samples, n_components]</span></dt><dd><p>X scores.</p>
</dd>
<dt><strong>y_scores_</strong><span class="classifier">array, [n_samples, n_components]</span></dt><dd><p>Y scores.</p>
</dd>
<dt><strong>x_rotations_</strong><span class="classifier">array, [p, n_components]</span></dt><dd><p>X block to latents rotations.</p>
</dd>
<dt><strong>y_rotations_</strong><span class="classifier">array, [q, n_components]</span></dt><dd><p>Y block to latents rotations.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">array-like</span></dt><dd><p>Number of iterations of the NIPALS inner loop for each
component.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PLSCanonical</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.cross_decomposition.PLSSVD.html#sklearn.cross_decomposition.PLSSVD" title="sklearn.cross_decomposition.PLSSVD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PLSSVD</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>For each component k, find the weights u, v that maximizes
max corr(Xk u, Yk v), such that <code class="docutils literal notranslate"><span class="pre">|u|</span> <span class="pre">=</span> <span class="pre">|v|</span> <span class="pre">=</span> <span class="pre">1</span></code></p>
<p>Note that it maximizes only the correlations between the scores.</p>
<p>The residual matrix of X (Xk+1) block is obtained by the deflation on the
current X score: x_score.</p>
<p>The residual matrix of Y (Yk+1) block is obtained by deflation on the
current Y score.</p>
<p class="rubric">References</p>
<p>Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with
emphasis on the two-block case. Technical Report 371, Department of
Statistics, University of Washington, Seattle, 2000.</p>
<p>In french but still a reference:
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.</p>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.cross_decomposition</span> <span class="kn">import</span> <span class="n">CCA</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span><span class="mf">0.</span><span class="p">,</span><span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span><span class="mf">2.</span><span class="p">,</span><span class="mf">2.</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.</span><span class="p">,</span><span class="mf">5.</span><span class="p">,</span><span class="mf">4.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">6.2</span><span class="p">,</span> <span class="mf">5.9</span><span class="p">],</span> <span class="p">[</span><span class="mf">11.9</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cca</span> <span class="o">=</span> <span class="n">CCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
<span class="go">CCA(n_components=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_c</span><span class="p">,</span> <span class="n">Y_c</span> <span class="o">=</span> <span class="n">cca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.fit" title="sklearn.cross_decomposition.CCA.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, Y)</p></td>
<td><p>Fit model to data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.fit_transform" title="sklearn.cross_decomposition.CCA.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Learn and apply the dimension reduction on the train data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.get_params" title="sklearn.cross_decomposition.CCA.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.inverse_transform" title="sklearn.cross_decomposition.CCA.inverse_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>(self, X)</p></td>
<td><p>Transform data back to its original space.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.predict" title="sklearn.cross_decomposition.CCA.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X[, copy])</p></td>
<td><p>Apply the dimension reduction learned on the train data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.score" title="sklearn.cross_decomposition.CCA.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination R^2 of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.set_params" title="sklearn.cross_decomposition.CCA.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.CCA.transform" title="sklearn.cross_decomposition.CCA.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X[, Y, copy])</p></td>
<td><p>Apply the dimension reduction learned on the train data.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_components=2</em>, <em class="sig-param">scale=True</em>, <em class="sig-param">max_iter=500</em>, <em class="sig-param">tol=1e-06</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_cca.py#L102"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">Y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_pls.py#L264"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit model to data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</dd>
<dt><strong>Y</strong><span class="classifier">array-like of shape (n_samples, n_targets)</span></dt><dd><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_pls.py#L500"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Learn and apply the dimension reduction on the train data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples, n_targets)</span></dt><dd><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>x_scores if Y is not given, (x_scores, y_scores) otherwise.</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.inverse_transform">
<code class="sig-name descname">inverse_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_pls.py#L448"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.inverse_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform data back to its original space.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_components)</span></dt><dd><p>New data, where n_samples is the number of samples
and n_components is the number of pls components.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>x_reconstructed</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd></dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This transformation will only be exact if n_components=n_features</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_pls.py#L475"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension reduction learned on the train data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean, default True</span></dt><dd><p>Whether to copy X and Y, or perform in-place normalization.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This call requires the estimation of a p x q matrix, which may
be an issue in high dimensional space.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a
precomputed kernel matrix or a list of generic objects instead,
shape = (n_samples, n_samples_fitted),
where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>R^2 of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The R2 score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor will use
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>. This will influence the
<code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>). To specify the
default value manually and avoid the warning, please either call
<a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a> directly or make a custom scorer with
<a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> (the built-in scorer <code class="docutils literal notranslate"><span class="pre">'r2'</span></code> uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code>).</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cross_decomposition.CCA.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">Y=None</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cross_decomposition/_pls.py#L410"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.CCA.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension reduction learned on the train data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</dd>
<dt><strong>Y</strong><span class="classifier">array-like of shape (n_samples, n_targets)</span></dt><dd><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean, default True</span></dt><dd><p>Whether to copy X and Y, or perform in-place normalization.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>x_scores if Y is not given, (x_scores, y_scores) otherwise.</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-cross-decomposition-cca">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cross_decomposition.CCA</span></code><a class="headerlink" href="#examples-using-sklearn-cross-decomposition-cca" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example simulates a multi-label document classification problem. The dataset is generated ..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_multilabel_thumb.png" src="../../_images/sphx_glr_plot_multilabel_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py"><span class="std std-ref">Multilabel classification</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with mu..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" src="../../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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